How to work with SAS xpt Data in Apache Spark using SQL

Apache Spark is a fast and general engine for large-scale data processing. When paired with the CData JDBC Driver for SAS xpt, Spark can work with live SAS xpt data. This article describes how to connect to and query SAS xpt data from a Spark shell.

The CData JDBC Driver offers unmatched performance for interacting with live SAS xpt data due to optimized data processing built into the driver. When you issue complex SQL queries to SAS xpt, the driver pushes supported SQL operations, like filters and aggregations, directly to SAS xpt and utilizes the embedded SQL engine to process unsupported operations (often SQL functions and JOIN operations) client-side. With built-in dynamic metadata querying, you can work with and analyze SAS xpt data using native data types.

Install the CData JDBC Driver for SAS xpt

Download the CData JDBC Driver for SAS xpt installer, unzip the package, and run the JAR file to install the driver.

Start a Spark Shell and Connect to SAS xpt Data

1. Open a terminal and start the Spark shell with the CData JDBC Driver for SAS xpt JAR file as the jars parameter: $ spark-shell --jars /CData/CData JDBC Driver for SAS xpt/lib/cdata.jdbc.sasxpt.jar
2. With the shell running, you can connect to SAS xpt with a JDBC URL and use the SQL Context load() function to read a table.

   Connecting to Local SASXpt Files

   You can connect to local SASXpt file by setting the URI to a folder containing SASXpt files.

   Connecting to S3 data source

   You can connect to Amazon S3 source to read SASXpt files. Set the following properties to connect:

   • URI: Set this to the folder within your bucket that you would like to connect to.
   • AWSAccessKey: Set this to your AWS account access key.
   • AWSSecretKey: Set this to your AWS account secret key.
   • TemporaryLocalFolder: Set this to the path, or URI, to the folder that is used to temporarily download SASXpt file(s).

   Connecting to Azure Data Lake Storage Gen2

   You can connect to ADLS Gen2 to read SASXpt files. Set the following properties to connect:

   • URI: Set this to the name of the file system and the name of the folder which contacts your SASXpt files.
   • AzureAccount: Set this to the name of the Azure Data Lake storage account.
   • AzureAccessKey: Set this to our Azure DataLakeStore Gen 2 storage account access key.
   • TemporaryLocalFolder: Set this to the path, or URI, to the folder that is used to temporarily download SASXpt file(s).

   Built-in Connection String Designer

   For assistance in constructing the JDBC URL, use the connection string designer built into the SAS xpt JDBC Driver. Either double-click the JAR file or execute the jar file from the command-line.

   java -jar cdata.jdbc.sasxpt.jar

   Fill in the connection properties and copy the connection string to the clipboard.

   Configure the connection to SAS xpt, using the connection string generated above.

   scala> val sasxpt_df = spark.sqlContext.read.format("jdbc").option("url", "jdbc:sasxpt:URI=C:/folder;").option("dbtable","SampleTable_1").option("driver","cdata.jdbc.sasxpt.SASXptDriver").load()
3. Once you connect and the data is loaded you will see the table schema displayed.

4. Register the SAS xpt data as a temporary table:

   scala> sasxpt_df.registerTable("sampletable_1")

5. Perform custom SQL queries against the Data using commands like the one below:

   scala> sasxpt_df.sqlContext.sql("SELECT Id, Column1 FROM SampleTable_1 WHERE Column2 = 100").collect.foreach(println)

   You will see the results displayed in the console, similar to the following:

Using the CData JDBC Driver for SAS xpt in Apache Spark, you are able to perform fast and complex analytics on SAS xpt data, combining the power and utility of Spark with your data. Download a free, 30 day trial of any of the 200+ CData JDBC Drivers and get started today.